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Related Experiment Video

Updated: Feb 17, 2026

Concentration of Metabolites from Low-density Planktonic Communities for Environmental Metabolomics using Nuclear Magnetic Resonance Spectroscopy

Concentration of Metabolites from Low-density Planktonic Communities for Environmental Metabolomics using Nuclear Magnetic Resonance Spectroscopy

Published on: April 7, 2012

Null diffusion-based enrichment for metabolomics data.

Sergio Picart-Armada^1,2,3, Francesc Fernández-Albert^1,2,4, Maria Vinaixa^5,6,7

¹Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, Barcelona, Spain.

|December 7, 2017

Summary

This summary is machine-generated.

This study introduces a new pathway enrichment method for metabolomics data, offering a network view of metabolic processes. The approach aids in generating hypotheses and identifying potential enzyme targets for further research.

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Last Updated: Feb 17, 2026

Concentration of Metabolites from Low-density Planktonic Communities for Environmental Metabolomics using Nuclear Magnetic Resonance Spectroscopy

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Published on: April 7, 2012

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A Strategy for Sensitive, Large Scale Quantitative Metabolomics

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

Area of Science:

Metabolomics
Systems Biology
Bioinformatics

Background:

Metabolomics identifies metabolite changes under varying conditions.
Pathway enrichment tools link these changes to biological processes.
Current methods lack a comprehensive network view of metabolomics data across molecular levels.

Purpose of the Study:

To develop a novel pathway enrichment procedure for metabolomics data analysis.
To provide a network-based overview of metabolic pathways and related biological entities.
To generate new hypotheses and identify potential enzymatic targets from metabolomics data.

Main Methods:

Utilized sub-network analysis on a graph representation of a reference database (KEGG).
Employed a null diffusive process accounting for network topology and pathway crosstalk for entry extraction.
Analyzed GC-MS and LC-MS data from cells depleted for an uncharacterized mitochondrial gene.
Partially validated findings using NMR-based 13C glucose labeling.

Main Results:

Developed a novel pathway enrichment procedure for metabolomics.
Generated a sub-pathway network including pathways, modules, enzymes, and reactions.
Identified key metabolic processes and potential targets in cells with mitochondrial gene depletion.
Provided partial validation of the method's utility.

Conclusions:

The proposed method offers a richer biological context for metabolomics data.
It facilitates hypothesis generation and identification of potential enzymatic targets.
This network-based approach enhances the interpretation of complex metabolomics datasets.